Predictive Models
PREDICTIVE MODELS FOR RECREATIONAL WATER
QUALITY
Swim advisories
or closings are issued by beach managers on the basis of standards for
concentrations of bacterial indicators—Escherichia coli (E. coli)
or enterococci for freshwaters and enterococci for marine waters. The
analytical methods for these organisms, however, take at least 18–24 hours to
complete. Recreational water-quality conditions may change during this time,
leading to erroneous assessments of public-health risk. As a result, some
agencies have turned to predictive modeling to obtain near-real-time estimates
of recreational water quality. Predictive models, developed through statistical
techniques such as multiple linear regression (MLR), use easily measured
environmental and water-quality variables to estimate bacterial-indicator
concentrations or the probability of exceeding target concentrations.
Predictive
modeling at Five Lake Erie beaches
NOWCASTING: an operational system at Huntington, Bay Village, Ohio
How can we develop a predictive model for our beaches?
Collecting better data for predictive models
Predictive modeling at
Five Lake Erie
beaches:
The USGS Ohio Water Science Center (Ohio WSC) has been working to develop predictive
models for five Lake Erie beaches in Ohio: Lakeview (Lorain, Ohio), Huntington
(Bay Village, Ohio), Edgewater Park and Villa Angela (Cleveland, Ohio), and
Lakeshore Park (Ashtabula, Ohio). See a detailed report at:
https://webarchive.library.unt.edu/eot2008/20081107222810/http://pubs.usgs.gov/sir/2006/5192/.
![beach map beach map](micro2/beach_page/beach_map.jpg)
NOWCASTING: an operation system at
Huntington, Bay Village, Ohio
At Huntington, investigations are further along than at other
beaches. Predictions based on the Huntington model have been available to the
public through an Internet-based
NOWCASTING system since May 30, 2006.
The
NOWCAST is like a weather forecast, in that it provides the probability (in
percent) that the bathing-water standard for E. coli will be exceeded.
(The Ohio single-sample bathing-water standard for E. coli is 235
colony-forming units/100 milliliters). So on any given morning, there could be
from a 1- to 100- percent probability that the standard would be exceeded. How
does one know when the probability presents too great a risk to go swimming?
Would you go swimming if there was an 80-percent probability that the standard
would be exceeded? What about a 25 percent chance? To help out, beach managers
established a threshold probability of 27 percent for Huntington based on
historical data. If the probability was greater than or equal to 27 percent,
than the beach was posted with an advisory on the NOWCAST.
How did the NOWCAST system perform in 2006?
NOWCASTS were provided to the public for 85 days during the recreational season
of 2006. The NOWCAST provided a correct response, 80 percent of time. False
positive responses were provided 10 percent of time; that means that the NOWCAST
incorrectly predicted that the standard was exceeded on 6 out of 59 days that
the standard was actually NOT exceeded. False negative responses were higher –
42 percent. That means that the NOWCAST incorrectly predicted that the standard
would NOT be exceeded on 11 out of 26 days that the standard was actually
exceeded.
Although the false negative rate for the
NOWCAST is higher than we would like, the NOWCAST still provides more accurate
information and better estimates of public health risk than the use of the
previous day’s E. coli (the current method used by most beach
managers.). During 2006, the previous day’s E. coli provided only 57
percent correct responses. False positives were provided 30 percent and false
negatives 72 percent of time.
We will continue to work to improve the
predictive ability of the NOWCAST in 2007.
How can we develop a predictive model for
our beaches?
To find out, in a step-by-step
fashion how to develop predictive models for your beaches, click on the
techniques report. The steps to develop
predictive models are data collection; exploratory data analysis; model
development, selection, and diagnosis; determination of model out values; and
model validation and refinement.
Collecting better data for predictive
models:
Predictive modeling is a dynamic process meant to augment existing
beach-monitoring programs. Models should be continuously validated and refined
to improve predictions.
The
USGS Ohio Water Science Center is working with Northeast Ohio Regional Sewer
District (NEORSD) to collect more accurate wave height data. In the past, wave
heights were estimated by field crews based on visual determinations and placed
into one of four categories; they are currently measured by use of a wave
measuring stick. To further improve wave-height measurements, the USGS installed
a buoy at Edgewater, Cleveland, Oh. The buoy is equipped with instrumentation
to measure wave heights and store the data. Data from the buoy are transmitted
hourly to a wireless access point.
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